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Merge branch 'develop' into cancel_partial_sell

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This commit is contained in:
Matthias
2022-10-02 08:38:18 +02:00
parent 47ef99f588 e686faf1bc
commit a5bc75b48c
24 changed files with 146 additions and 79 deletions
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1
freqtrade/constants.py
View File

@@ -567,6 +567,7 @@ CONF_SCHEMA = {
"properties": {
"test_size": {"type": "number"},
"random_state": {"type": "integer"},
"shuffle": {"type": "boolean", "default": False}
},
},
"model_training_parameters": {

31
freqtrade/exchange/binance.py
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@@ -68,6 +68,37 @@ class Binance(Exchange):
tickers = deep_merge_dicts(bidsasks, tickers, allow_null_overrides=False)
return tickers
@retrier
def additional_exchange_init(self) -> None:
"""
Additional exchange initialization logic.
.api will be available at this point.
Must be overridden in child methods if required.
"""
try:
if self.trading_mode == TradingMode.FUTURES and not self._config['dry_run']:
position_side = self._api.fapiPrivateGetPositionsideDual()
self._log_exchange_response('position_side_setting', position_side)
assets_margin = self._api.fapiPrivateGetMultiAssetsMargin()
self._log_exchange_response('multi_asset_margin', assets_margin)
msg = ""
if position_side.get('dualSidePosition') is True:
msg += (
"\nHedge Mode is not supported by freqtrade. "
"Please change 'Position Mode' on your binance futures account.")
if assets_margin.get('multiAssetsMargin') is True:
msg += ("\nMulti-Asset Mode is not supported by freqtrade. "
"Please change 'Asset Mode' on your binance futures account.")
if msg:
raise OperationalException(msg)
except ccxt.DDoSProtection as e:
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not set leverage due to {e.__class__.__name__}. Message: {e}') from e
except ccxt.BaseError as e:
raise OperationalException(e) from e
@retrier
def _set_leverage(
self,

2
freqtrade/exchange/exchange.py
View File

@@ -1292,7 +1292,7 @@ class Exchange:
order = self.fetch_order(order_id, pair)
except InvalidOrderException:
logger.warning(f"Could not fetch cancelled order {order_id}.")
order = {'fee': {}, 'status': 'canceled', 'amount': amount, 'info': {}}
order = {'id': order_id, 'fee': {}, 'status': 'canceled', 'amount': amount, 'info': {}}
return order

3
freqtrade/exchange/okx.py
View File

@@ -78,7 +78,8 @@ class Okx(Exchange):
raise DDosProtection(e) from e
except (ccxt.NetworkError, ccxt.ExchangeError) as e:
raise TemporaryError(
f'Could not set leverage due to {e.__class__.__name__}. Message: {e}') from e
f'Error in additional_exchange_init due to {e.__class__.__name__}. Message: {e}'
) from e
except ccxt.BaseError as e:
raise OperationalException(e) from e

2
freqtrade/freqai/base_models/BaseClassifierModel.py
View File

@@ -92,7 +92,7 @@ class BaseClassifierModel(IFreqaiModel):
filtered_df = dk.normalize_data_from_metadata(filtered_df)
dk.data_dictionary["prediction_features"] = filtered_df
self.data_cleaning_predict(dk, filtered_df)
self.data_cleaning_predict(dk)
predictions = self.model.predict(dk.data_dictionary["prediction_features"])
pred_df = DataFrame(predictions, columns=dk.label_list)

2
freqtrade/freqai/base_models/BaseRegressionModel.py
View File

@@ -92,7 +92,7 @@ class BaseRegressionModel(IFreqaiModel):
dk.data_dictionary["prediction_features"] = filtered_df
# optional additional data cleaning/analysis
self.data_cleaning_predict(dk, filtered_df)
self.data_cleaning_predict(dk)
predictions = self.model.predict(dk.data_dictionary["prediction_features"])
pred_df = DataFrame(predictions, columns=dk.label_list)

2
freqtrade/freqai/data_drawer.py
View File

@@ -423,7 +423,7 @@ class FreqaiDataDrawer:
dk.data["data_path"] = str(dk.data_path)
dk.data["model_filename"] = str(dk.model_filename)
dk.data["training_features_list"] = list(dk.data_dictionary["train_features"].columns)
dk.data["training_features_list"] = dk.training_features_list
dk.data["label_list"] = dk.label_list
# store the metadata
with open(save_path / f"{dk.model_filename}_metadata.json", "w") as fp:

13
freqtrade/freqai/data_kitchen.py
View File

@@ -134,20 +134,15 @@ class FreqaiDataKitchen:
"""
feat_dict = self.freqai_config["feature_parameters"]
if 'shuffle' not in self.freqai_config['data_split_parameters']:
self.freqai_config["data_split_parameters"].update({'shuffle': False})
weights: npt.ArrayLike
if feat_dict.get("weight_factor", 0) > 0:
weights = self.set_weights_higher_recent(len(filtered_dataframe))
else:
weights = np.ones(len(filtered_dataframe))
if feat_dict.get("stratify_training_data", 0) > 0:
stratification = np.zeros(len(filtered_dataframe))
for i in range(1, len(stratification)):
if i % feat_dict.get("stratify_training_data", 0) == 0:
stratification[i] = 1
else:
stratification = None
if self.freqai_config.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
(
train_features,
@@ -160,7 +155,6 @@ class FreqaiDataKitchen:
filtered_dataframe[: filtered_dataframe.shape[0]],
labels,
weights,
stratify=stratification,
**self.config["freqai"]["data_split_parameters"],
)
else:
@@ -881,6 +875,7 @@ class FreqaiDataKitchen:
"""
column_names = dataframe.columns
features = [c for c in column_names if "%" in c]
if not features:
raise OperationalException("Could not find any features!")

17
freqtrade/freqai/freqai_interface.py
View File

@@ -275,7 +275,8 @@ class IFreqaiModel(ABC):
if dk.check_if_backtest_prediction_exists():
self.dd.load_metadata(dk)
self.check_if_feature_list_matches_strategy(dataframe_train, dk)
dk.find_features(dataframe_train)
self.check_if_feature_list_matches_strategy(dk)
append_df = dk.get_backtesting_prediction()
dk.append_predictions(append_df)
else:
@@ -296,7 +297,6 @@ class IFreqaiModel(ABC):
else:
self.model = self.dd.load_data(pair, dk)
# self.check_if_feature_list_matches_strategy(dataframe_train, dk)
pred_df, do_preds = self.predict(dataframe_backtest, dk)
append_df = dk.get_predictions_to_append(pred_df, do_preds)
dk.append_predictions(append_df)
@@ -420,7 +420,7 @@ class IFreqaiModel(ABC):
return
def check_if_feature_list_matches_strategy(
self, dataframe: DataFrame, dk: FreqaiDataKitchen
self, dk: FreqaiDataKitchen
) -> None:
"""
Ensure user is passing the proper feature set if they are reusing an `identifier` pointing
@@ -429,11 +429,12 @@ class IFreqaiModel(ABC):
:param dk: FreqaiDataKitchen = non-persistent data container/analyzer for
current coin/bot loop
"""
dk.find_features(dataframe)
if "training_features_list_raw" in dk.data:
feature_list = dk.data["training_features_list_raw"]
else:
feature_list = dk.data['training_features_list']
if dk.training_features_list != feature_list:
raise OperationalException(
"Trying to access pretrained model with `identifier` "
@@ -481,13 +482,16 @@ class IFreqaiModel(ABC):
if self.freqai_info["feature_parameters"].get('noise_standard_deviation', 0):
dk.add_noise_to_training_features()
def data_cleaning_predict(self, dk: FreqaiDataKitchen, dataframe: DataFrame) -> None:
def data_cleaning_predict(self, dk: FreqaiDataKitchen) -> None:
"""
Base data cleaning method for predict.
Functions here are complementary to the functions of data_cleaning_train.
"""
ft_params = self.freqai_info["feature_parameters"]
# ensure user is feeding the correct indicators to the model
self.check_if_feature_list_matches_strategy(dk)
if ft_params.get('inlier_metric_window', 0):
dk.compute_inlier_metric(set_='predict')
@@ -505,9 +509,6 @@ class IFreqaiModel(ABC):
if ft_params.get("use_DBSCAN_to_remove_outliers", False):
dk.use_DBSCAN_to_remove_outliers(predict=True)
# ensure user is feeding the correct indicators to the model
self.check_if_feature_list_matches_strategy(dk.data_dictionary['prediction_features'], dk)
def model_exists(self, dk: FreqaiDataKitchen) -> bool:
"""
Given a pair and path, check if a model already exists

6
freqtrade/rpc/api_server/webserver.py
View File

@@ -198,8 +198,10 @@ class ApiServer(RPCHandler):
logger.debug(f"Found message of type: {message.get('type')}")
# Broadcast it
await self._ws_channel_manager.broadcast(message)
# Sleep, make this configurable?
await asyncio.sleep(0.1)
# Limit messages per sec.
# Could cause problems with queue size if too low, and
# problems with network traffik if too high.
await asyncio.sleep(0.001)
except asyncio.CancelledError:
pass

2
freqtrade/rpc/discord.py
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@@ -30,9 +30,9 @@ class Discord(Webhook):
pass
def send_msg(self, msg) -> None:
logger.info(f"Sending discord message: {msg}")
if msg['type'].value in self.config['discord']:
logger.info(f"Sending discord message: {msg}")
msg['strategy'] = self.strategy
msg['timeframe'] = self.timeframe

8
freqtrade/rpc/webhook.py
View File

@@ -61,6 +61,14 @@ class Webhook(RPCHandler):
RPCMessageType.STARTUP,
RPCMessageType.WARNING):
valuedict = whconfig.get('webhookstatus')
elif msg['type'] in (
RPCMessageType.PROTECTION_TRIGGER,
RPCMessageType.PROTECTION_TRIGGER_GLOBAL,
RPCMessageType.WHITELIST,
RPCMessageType.ANALYZED_DF,
RPCMessageType.STRATEGY_MSG):
# Don't fail for non-implemented types
return
else:
raise NotImplementedError('Unknown message type: {}'.format(msg['type']))
if not valuedict: